Optimizing Alarm Design: A Comparison of Delay-Timers, Counters, and Time-Deadbands
Bibliographic record
Abstract
Alarm systems are crucial for ensuring the safety and efficiency of industrial operations. However, operators often face an overwhelming number of alarms, many of which are false or nuisance alarms. To address this issue and to enhance alarm system performance, techniques such as delay-timers, up/down counters, and time-deadbands are employed in alarm design. This paper provides a comprehensive comparative analysis of these techniques using performance indices, namely, False Alarm Rate (FAR), Missed Alarm Rate (MAR), and Expected Detection Delay (EDD). The study aims to identify the optimal technique yielding the best trade-off between accuracy and delay. The contributions of this paper are twofold: (1) A systematic performance analysis of the three techniques is conducted. This includes individual evaluations under varying alarm thresholds, accuracy comparisons using Receiver Operating Characteristics (ROC) curves, and a generalized comparison combining accuracy and delay. (2) A novel algorithm is proposed to select the optimal alarm reduction technique for a given detection delay, allowing operators to meet practical constraints in alarm system design efficiently. The effectiveness of the proposed approach is demonstrated through numerical examples, with results presented as augmented matrices and visualized through color-coded plots. This simultaneous comparison of techniques facilitates the design of more efficient and reliable alarm systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".